(Auteur) The overarching goal of this study was to map specific crop types in the Central Valley, California and estimate the effect of classification uncertainty on the calculation of crop evapotranspiration (ETc). A phenology-based classification (PBC) approach was developed to identify crop types based on phenological and spectral metrics derived from the time series of Landsat TM/ETM_ imagery. Phenological metrics, calculated by fitting asymmetric double sigmoid functions to temporal profiles of enhanced vegetation index (EVI), were capable of separating crop types with distinct crop calendars. An innovative method was used to compute spectral metrics to represent crops' spectral characteristics at certain phenological stages instead of any specific imaging date. Crop mapping using these metrics showed a stable performance without influences of low-quality data and inter-annual differences in imaging dates. The requirement for ground reference data by the PBC approach was low because classification algorithms were mostly built according to the knowledge on crop calendars and agricultural practices. Techniques including image segmentation, data fusion with MODIS imagery, and decision tree were incorporated to make the approach effective and efficient. Though moderate accuracy (~65.0 percent) was achieved, ETc calculated by the Food and Agriculture Organization (FAO) 56 method showed that the estimate of water use was not likely to be significantly affected by the classification error in PBC. All these advantages imply the strength of the PBC approach in the regular crop mapping of the Central Valley.

Mapping crop types, irrigated areas, and cropping intensities in heterogeneous landscapes of southern India using multi-temporal medium-resolution imagery: implications for assessing water use in agriculture

(Auteur) In regions of water scarcity, mapping individual crops, cropping intensities and irrigation can contribute significantly to understanding agricultural water use. But such mapping is challenging in landscapes dominated by small-scale traditional agricultural land holdings with high spatial and temporal heterogeneity. Here, we assessed the benefit of using multi-temporal 24 m resolution LISS-III imagery to characterize cropping systems in the Malaprabha basin of southern India. We used hierarchical stacked supervised classification to create three increasingly detailed maps showing: (a) single rainfed paddy rice versus continuously irrigated sugarcane, (b) irrigated versus rainfed areas, and (c) multiple cropping. Although increasing detail was accompanied by decreasing overall accuracies (89 percent, 74.6 percent and 60.1 percent respectively), using multi-temporal imagery out-performed single imagery alone in all cases. Results also led to higher estimates of total (69.8 percent) and irrigated (34.7 percent) cropland than previous single-imagery studies and census data, revealing the high uncertainty in crop estimates in this region.

(Auteur) We worked on the assumption that agricultural systems shaped the landscape through human cropping practices, and that the resulting landscape can be described with a set of coarse resolution satellite-derived metrics (spectral, textural, temporal, and spatial metrics). A Random Forest classification model was developed at the village scale in South Mali, based on 100 samples, with data on the main type of agricultural system in each village (three-class typology), and 30 MODIS-derived and socio-environmental metrics calculated on agricultural areas. The model was found to perform well (overall accuracy of 60 percent) and was stable. Class A (food crops) and B (intensive agriculture) displayed good producer's accuracy (70 percent and 67 percent, respectively), while class C (mixed agriculture) was less accurate (50 percent). The most important metrics were shown to be the annual mean of NDVI, followed by the phenology transition dates and texture metrics. However, when considering each set of metrics separately, texture emerged as the most discriminating factor (with 53 percent of global accuracy). This result, i.e., that even coarse resolution imagery contains textural information that can be used for crop mapping, is new. Such maps could be used in food security systems as an indicator of system vulnerability, or as spatial inputs for crop yield models.

(Auteur) Quantifying evapotranspiration (ET) is a key element for achieving better water management, especially in regions where agriculture is the main water consumer. A hybrid model combining the SEBAL and RESET models (S-RESET) was developed to effectively estimate actual ET (water use) of the agriculture sector around the Phoenix metropolitan area. To examine how irrigated agriculture water consumption varies with climate, the S-RESET model was applied under wet and dry climatic conditions. Results show that the average ET for active agriculture is 9.3 mm/day (_ 3.8mm/day) during the study period. Seasonal water use was 438 mm for 2000 (drought) and 494 mm for 2008 (wet). Based on the seasonal ET, we concluded that farmers in arid region use the same amount of water regardless of climatic conditions, implying that the agriculture sector as a whole may not be sensitive to drought as long as there is sufficient water from irrigation. This finding carries significant implications for the region's water security.

(Auteur) In this paper, we propose an innovative method for identifying irrigated areas and quantifying the blue evapotranspiration (ETb), or irrigation water evapotranspired from the field. The method compares actual ET (ETactual), or crop water use, values from the Global Land Data Assimilation System (GLDAS) and remote sensing based ETactual estimates obtained from Meteosat Second Generation (MSG) satellites. Since GLDAS simulations do not account for extra water supply due to irrigation, it is expected that they underestimate ETactual during the cropping season in irrigated areas. However, remote sensing techniques based on the energy balance are able to observe the total ETactual. In order to isolate irrigation effects from other fluctuations that may lead to discrepancies between the different ETactual products, the bias between model simulations and remote sensing observations was estimated using reference targets of rainfed (non-irrigated) croplands on a daily basis in different areas across the study region (Europe). Analysis of the yearly values of ETb (irrigated area and volume obtained for croplands in Europe for 2008) showed that the method identified irrigation when yearly values were higher than 50 mm. The accuracy of the method was assessed by analyzing the spatial representativity of the calculated biases and evaluating the daily ETb values obtained. The irrigated areas were compared with the results provided by Siebert et al.(2007) and Thenkabail et al.(2009b), obtaining a spatial match of 47 and 72 percent, respectively, with overestimation of irrigated area on a country scale. Additional evaluation with the ETb results of Mekonnen and Hoekstra (2011) showed 75 percent of overlap for _50 mm range. Finally, validation with in situ data on irrigation volumes proved the cogency of our method with less than 20 percent difference between derived and measured values.